Code/deferral/process_results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import yaml\n",
    "import os\n",
    "import random\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "with open('../../Data_and_models/alert_model/model_properties.pickle', 'rb') as infile:\n",
    "    model_properties = pickle.load(infile)\n",
    "\n",
    "with open('../data/dataset_cfg.yaml', 'r') as infile:\n",
    "    data_cfg = yaml.safe_load(infile)\n",
    "\n",
    "with open('cfg.yaml', 'r') as infile:\n",
    "    cfg = yaml.safe_load(infile)\n",
    "\n",
    "cat_dict = data_cfg['categorical_dict']\n",
    "\n",
    "def cat_checker(data, features, cat_dict):\n",
    "    new_data = data.copy()\n",
    "    for feature in features:\n",
    "        if new_data[feature].dtype != 'category':\n",
    "            new_data[feature] = pd.Categorical(new_data[feature].values, categories=cat_dict[feature])\n",
    "        elif new_data[feature].dtype.categories.to_list() != cat_dict[feature]:\n",
    "            new_data[feature] = pd.Categorical(new_data[feature].values, categories=cat_dict[feature])\n",
    "    \n",
    "    return new_data\n",
    "\n",
    "CATEGORICAL_COLS = data_cfg['data_cols']['categorical']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alert_rate</th>\n",
       "      <th>cost</th>\n",
       "      <th>sub</th>\n",
       "      <th>fp</th>\n",
       "      <th>fn</th>\n",
       "      <th>tp</th>\n",
       "      <th>tn</th>\n",
       "      <th>correct</th>\n",
       "      <th>incorrect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301655</td>\n",
       "      <td>0.003310</td>\n",
       "      <td>0.111462</td>\n",
       "      <td>0.583573</td>\n",
       "      <td>0.695035</td>\n",
       "      <td>0.304965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.249773</td>\n",
       "      <td>0.018721</td>\n",
       "      <td>0.094225</td>\n",
       "      <td>0.637282</td>\n",
       "      <td>0.731506</td>\n",
       "      <td>0.268494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.169310</td>\n",
       "      <td>0.056243</td>\n",
       "      <td>0.058529</td>\n",
       "      <td>0.715918</td>\n",
       "      <td>0.774447</td>\n",
       "      <td>0.225553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.343658</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>0.054653</td>\n",
       "      <td>0.598708</td>\n",
       "      <td>0.653361</td>\n",
       "      <td>0.346639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.305627</td>\n",
       "      <td>0.014458</td>\n",
       "      <td>0.042365</td>\n",
       "      <td>0.637549</td>\n",
       "      <td>0.679915</td>\n",
       "      <td>0.320085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.2850</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103872</td>\n",
       "      <td>0.026646</td>\n",
       "      <td>0.030988</td>\n",
       "      <td>0.838494</td>\n",
       "      <td>0.869482</td>\n",
       "      <td>0.130518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alert_rate    cost       sub        fp        fn        tp        tn  \\\n",
       "0        0.05  0.0114  1.000000  0.301655  0.003310  0.111462  0.583573   \n",
       "1        0.05  0.0570  0.583333  0.249773  0.018721  0.094225  0.637282   \n",
       "2        0.05  0.2850  1.000000  0.169310  0.056243  0.058529  0.715918   \n",
       "3        0.15  0.0114  1.000000  0.343658  0.002981  0.054653  0.598708   \n",
       "4        0.15  0.0570  0.583333  0.305627  0.014458  0.042365  0.637549   \n",
       "5        0.15  0.2850  1.000000  0.103872  0.026646  0.030988  0.838494   \n",
       "\n",
       "    correct  incorrect  \n",
       "0  0.695035   0.304965  \n",
       "1  0.731506   0.268494  \n",
       "2  0.774447   0.225553  \n",
       "3  0.653361   0.346639  \n",
       "4  0.679915   0.320085  \n",
       "5  0.869482   0.130518  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = dict()\n",
    "for dir in os.listdir('../../Data_and_models/testbed/'):\n",
    "    train[dir] = dict()\n",
    "    for dir2 in os.listdir(f'../../Data_and_models/testbed/{dir}/train_alert/'):\n",
    "        train[dir][dir2] = pd.read_parquet(f'../../Data_and_models/testbed/{dir}/train_alert/{dir2}/train.parquet')\n",
    "        train_properties = pd.DataFrame(columns = ['alert_rate','sub','cost','seed','fp','fn','tp','tn','correct','incorrect'])\n",
    "\n",
    "train_properties_rescaled = pd.DataFrame(columns = ['alert_rate','sub','cost','seed','fp','fn','tp','tn','correct','incorrect'])\n",
    "\n",
    "for setting in train:\n",
    "    alert_rate = float(setting.split('-')[0].split('_')[1])\n",
    "    if len(setting.split('-')) == 4:\n",
    "        l = float(setting.split('-')[3].split('_')[1])\n",
    "        sub = float(setting.split('-')[2].split('_')[1])\n",
    "    else:\n",
    "        l = float(setting.split('-')[2].split('_')[1])\n",
    "        sub = 1\n",
    "    for seed in train[setting]:\n",
    "        train_y = train[setting][seed]['fraud_bool']\n",
    "        train_preds = train[setting][seed]['decision']\n",
    "        tn, fp, fn, tp = confusion_matrix(y_true = train_y, y_pred = train_preds).ravel()\n",
    "        train_properties = train_properties.append(pd.Series([alert_rate,sub,l,seed,fp/(len(train_y)),fn/(len(train_y)),tp/(len(train_y)),tn/(len(train_y)),(tp+tn)/(len(train_y)),(fp+fn)/(len(train_y))], index = train_properties.columns), ignore_index=True)\n",
    "\n",
    "train_properties.groupby(by = ['alert_rate','cost']).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.calibration import calibration_curve\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "scens = ['alert_0.05-data_0.05','alert_0.15-data_0.05']\n",
    "costs = [0.0114,0.057,0.285]\n",
    "\n",
    "auc = pd.DataFrame()\n",
    "ece = pd.DataFrame()\n",
    "for scen in scens:\n",
    "    for l in costs:\n",
    "        alert_rate = float(scen.split('-')[0].split('_')[1])\n",
    "        if len(scen.split('-')) == 2:\n",
    "            sub = 1\n",
    "        else:\n",
    "            sub = float(scen.split('-')[2].split('_')[1])\n",
    "        with open(f'../../Data_and_models/deferral/test_preds/{scen}-l_{l}/ova.pkl', 'rb') as infile:\n",
    "            ova = pickle.load(infile)\n",
    "        \n",
    "        with open(f'../../Data_and_models/deferral/test_preds/{scen}-l_{l}/deccaf.pkl', 'rb') as infile:\n",
    "            deccaf = pickle.load(infile)\n",
    "\n",
    "        \n",
    "\n",
    "        a = pd.read_parquet(f'../../Data_and_models/testbed/{scen}-l_{l}/test/test_expert_pred.parquet').drop(columns = 'model#0')\n",
    "        b = pd.read_parquet(f'../../Data_and_models/testbed/{scen}-l_{l}/test/test.parquet')['fraud_label']\n",
    "\n",
    "        data = pd.read_parquet(f'../../Data_and_models/data/alerts/{scen}.parquet')\n",
    "        val = data.loc[data['month'] == 6]\n",
    "        e_c = val['fraud_bool'].replace([0,1], [l,1]).mean()\n",
    "        reb_1 = (val['fraud_bool'].mean()/e_c)\n",
    "        reb_0 = (1-val['fraud_bool']).mean()*l/e_c\n",
    "        test = pd.read_parquet(f'../../Data_and_models/testbed/{scen}-l_{l}/test/test.parquet')\n",
    "        nmin = len(test.loc[test['fraud_label'] == 0])\n",
    "        nmax = int(nmin*reb_1/reb_0)\n",
    "\n",
    "        \n",
    "        oversampled = pd.concat([test.loc[test['fraud_label'] == 0], test.loc[test['fraud_label'] == 1].sample(replace=True, n = nmax)]).index\n",
    "        for seed in ova:\n",
    "            for expert in a.columns:\n",
    "                outcomes = (a.loc[oversampled,expert] == b.loc[oversampled]).astype(int)\n",
    "                auc.loc[f'{scen}-l_{l}-{seed}-ova',expert] = roc_auc_score(y_true = outcomes, y_score = ova[seed].loc[oversampled,expert])\n",
    "                prob_true, prob_pred = calibration_curve(y_true = outcomes, y_prob = ova[seed].loc[oversampled,expert], strategy='quantile', n_bins = 10)\n",
    "                ece.loc[f'{scen}-l_{l}-{seed}-ova',expert] = np.mean(np.abs(prob_true - prob_pred))\n",
    "                \n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-ova','alert_rate'] = alert_rate\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-ova','seed'] = seed\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-ova','model'] = 'ova'\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-ova','sub'] = sub\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-ova','cost'] = l\n",
    "\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-ova','alert_rate'] = alert_rate\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-ova','seed'] = seed\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-ova','model'] = 'ova'\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-ova','sub'] = sub\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-ova','cost'] = l\n",
    "        \n",
    "        for seed in deccaf:\n",
    "            for expert in a.columns:\n",
    "                outcomes =  (a.loc[oversampled,expert] == b.loc[oversampled]).astype(int)\n",
    "                auc.loc[f'{scen}-l_{l}-{seed}-deccaf',expert] = roc_auc_score(y_true = outcomes, y_score = deccaf[seed].loc[oversampled,expert])\n",
    "                prob_true, prob_pred = calibration_curve(y_true = outcomes, y_prob = deccaf[seed].loc[oversampled,expert], strategy='quantile', n_bins = 10)\n",
    "                ece.loc[f'{scen}-l_{l}-{seed}-deccaf',expert] = np.mean(np.abs(prob_true - prob_pred))\n",
    "                \n",
    "            \n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-deccaf','alert_rate'] = alert_rate\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-deccaf','seed'] = seed\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-deccaf','model'] = 'deccaf'\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-deccaf','sub'] = sub\n",
    "            auc.loc[f'{scen}-l_{l}-{seed}-deccaf','cost'] = l\n",
    "\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-deccaf','alert_rate'] = alert_rate\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-deccaf','seed'] = seed\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-deccaf','model'] = 'deccaf'\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-deccaf','sub'] = sub\n",
    "            ece.loc[f'{scen}-l_{l}-{seed}-deccaf','cost'] = l\n",
    "\n",
    "a = auc.loc[auc['alert_rate'] == 0.15].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.set_style(\"whitegrid\")\n",
    "f, ax = plt.subplots(figsize=(6, 6), ncols=2, nrows = 2, sharey = 'row', sharex='col', layout = 'tight')\n",
    "\n",
    "sns.lineplot(ax = ax[0][1],data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "a = auc.loc[auc['alert_rate'] == 0.05].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.lineplot(ax = ax[0][0], data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "\n",
    "\n",
    "ax[0][0].set_title(\"Alert Rate = 0.05\")\n",
    "ax[0][1].set_title(\"Alert Rate = 0.15\")\n",
    "\n",
    "for x in ax[0]:\n",
    "    x.set_ylabel('ROC-AUC')\n",
    "    x.set_xlabel(r'Value of $\\lambda$')\n",
    "    x.set_xticks(np.arange(0,0.30001,0.05))\n",
    "    x.set_xlim([0,0.3])\n",
    "    x.set_ylim([0.44,0.645])\n",
    "    x.set_yticks(np.arange(0.45,0.66,0.05))\n",
    "\n",
    "ax[0][0].axhline(y=0.50,xmin=0,xmax=3,c=\"black\",linewidth=1,zorder=1, linestyle = '--')\n",
    "ax[0][1].axhline(y=0.50,xmin=0,xmax=3,c=\"black\",linewidth=1,zorder=1, linestyle = '--')\n",
    "\n",
    "\n",
    "a = ece.loc[ece['alert_rate'] == 0.15].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a['value'] = a['value']*100\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "sns.lineplot(ax = ax[1][1],data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "a = ece.loc[ece['alert_rate'] == 0.05].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a['value'] = a['value']*100\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.lineplot(ax = ax[1][0], data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "\n",
    "for x in ax[1]:\n",
    "    x.set_xlabel(r'Value of $\\lambda$')\n",
    "    x.set_xticks(np.arange(0,0.30001,0.05))\n",
    "    x.set_xlim([0,0.3])\n",
    "    x.set_ylabel('ECE(%)')\n",
    "    \n",
    "\n",
    "#plt.yticks(np.arange(0.45,0.66,0.05))\n",
    "\n",
    "plt.savefig(\"ROC-AUC_individual.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_51497/1280302037.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ROC-AUC_individual_horiz.pdf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"pdf\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"tight\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    385\u001b[0m     \"\"\"\n\u001b[1;32m    386\u001b[0m     \u001b[0m_warn_if_gui_out_of_main_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_get_backend_mod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib_inline/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     90\u001b[0m             display(\n\u001b[1;32m     91\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m             )\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m             \u001b[0mpublish_display_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mformat_dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m                 \u001b[0;31m# nothing to display (e.g. _ipython_display_ took over)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/decorator.py\u001b[0m in \u001b[0;36mfun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mkwsyntax\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mcaller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextras\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m     \u001b[0mfun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2293\u001b[0m                 )\n\u001b[1;32m   2294\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_draw_disabled\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnullcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2295\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2297\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2844\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2845\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 2846\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   2847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2848\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0msfig\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubfigs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3053\u001b[0m                 \u001b[0martists\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3054\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3055\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_title_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3056\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3057\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxison\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_title_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2989\u001b[0m                 if (ax.xaxis.get_ticks_position() in ['top', 'unknown']\n\u001b[1;32m   2990\u001b[0m                         or ax.xaxis.get_label_position() == 'top'):\n\u001b[0;32m-> 2991\u001b[0;31m                     \u001b[0mbb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2992\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2993\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;34m'outline'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_tightbbox\u001b[0;34m(self, renderer, for_layout_only)\u001b[0m\n\u001b[1;32m   1103\u001b[0m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1105\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_label_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1107\u001b[0m         \u001b[0;31m# go back to just this axis's tick labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_label_position\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2081\u001b[0m         \u001b[0;31m# get bounding boxes for this axis and any siblings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2082\u001b[0m         \u001b[0;31m# that have been set by `fig.align_xlabels()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2083\u001b[0;31m         \u001b[0mbboxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbboxes2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick_boxes_siblings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2084\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2085\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick_boxes_siblings\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1878\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_siblings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{axis_name}axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1880\u001b[0;31m             \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1881\u001b[0m             \u001b[0mtlb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlb2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick_bboxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks_to_draw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1882\u001b[0m             \u001b[0mbboxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtlb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1044\u001b[0m         \"\"\"\n\u001b[1;32m   1045\u001b[0m         \u001b[0mmajor_locs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m         \u001b[0mmajor_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1047\u001b[0m         \u001b[0mmajor_ticks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36mformat_ticks\u001b[0;34m(self, values)\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;34m\"\"\"Return the tick labels for all the ticks at once.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mformat_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniforge3/envs/deccaf-env/lib/python3.7/site-packages/matplotlib/ticker.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;34m\"\"\"Return the tick labels for all the ticks at once.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mformat_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "a = auc.loc[auc['alert_rate'] == 0.15].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.set_style(\"whitegrid\")\n",
    "f, ax = plt.subplots(figsize=(12, 3), ncols=4, nrows = 1, layout = 'tight')\n",
    "\n",
    "sns.lineplot(ax = ax[1],data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "a = auc.loc[auc['alert_rate'] == 0.05].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.lineplot(ax = ax[0], data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "\n",
    "\n",
    "ax[0].set_title(\"Alert Rate = 0.05\")\n",
    "ax[1].set_title(\"Alert Rate = 0.15\")\n",
    "\n",
    "for x in [ax[0],ax[1]]:\n",
    "    x.set_ylabel('ROC-AUC')\n",
    "    x.set_xlabel(r'Value of $\\lambda$')\n",
    "    x.set_xticks(np.arange(0,0.30001,0.05))\n",
    "    x.set_xlim([0,0.3])\n",
    "    x.set_ylim([0.44,0.645])\n",
    "    x.set_yticks(np.arange(0.45,0.66,0.05))\n",
    "\n",
    "ax[0].axhline(y=0.50,xmin=0,xmax=3,c=\"black\",linewidth=1,zorder=1, linestyle = '--')\n",
    "ax[1].axhline(y=0.50,xmin=0,xmax=3,c=\"black\",linewidth=1,zorder=1, linestyle = '--')\n",
    "\n",
    "\n",
    "a = ece.loc[ece['alert_rate'] == 0.15].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a['value'] = a['value']*100\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "sns.lineplot(ax = ax[3],data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "a = ece.loc[ece['alert_rate'] == 0.05].melt(id_vars=['seed','model','sub','cost','alert_rate'])\n",
    "a['value'] = a['value']*100\n",
    "a.replace(['deccaf','ova'],['HEM','OvA'], inplace = True)\n",
    "a.rename(columns={'model': 'Method'}, inplace=True)\n",
    "#a.loc[a['alert_rate'] == 0.05]\n",
    "sns.lineplot(ax = ax[2], data = a, x = 'cost', y = 'value',hue = 'Method', markers = True, marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "\n",
    "\n",
    "ax[2].set_title(\"Alert Rate = 0.05\")\n",
    "ax[3].set_title(\"Alert Rate = 0.15\")\n",
    "\n",
    "\n",
    "for x in [ax[2],ax[3]]:\n",
    "    x.set_xlabel(r'Value of $\\lambda$')\n",
    "    x.set_xticks(np.arange(0,0.30001,0.05))\n",
    "    x.set_xlim([0,0.3])\n",
    "    x.set_ylabel('ECE(%)')\n",
    "    \n",
    "\n",
    "#plt.yticks(np.arange(0.45,0.66,0.05))\n",
    "\n",
    "plt.savefig(\"ROC-AUC_individual_horiz.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "test_env_df = pd.DataFrame(columns = ['scen','cost','sub', 'alert_rate','training_seed',\n",
    "                                        'model',\n",
    "                                        'batch_size',\n",
    "                                        'batch_seed', \n",
    "                                        'absence_rate', \n",
    "                                        'absence_seed', \n",
    "                                        'distribution', \n",
    "                                        'distribution_std', \n",
    "                                        'distribution_seed', \n",
    "                                        'deferral_rate',\n",
    "                                        'pool',\n",
    "                                        'n_errors',\n",
    "                                        'tp',\n",
    "                                        'fp',\n",
    "                                        'fn',\n",
    "                                        'tn',\n",
    "                                        'tpr',\n",
    "                                        'fpr',\n",
    "                                        'fpr_disp'])\n",
    "\n",
    "\n",
    "\n",
    "costs = cfg['costs']\n",
    "\n",
    "if not os.path.isfile('../../Data_and_models/deferral/def_results_alert/results.parquet'):\n",
    "    for scen in os.listdir('../../Data_and_models/data/alerts'):\n",
    "        if len(scen.split('-')) == 3:\n",
    "            sub_test_bool = True\n",
    "        else:\n",
    "            sub_test_bool = False\n",
    "        for l in costs:\n",
    "            if sub_test_bool and (l not in cfg['run_sub']):\n",
    "                continue\n",
    "            scen = scen.split('.parquet')[0]\n",
    "            print(f'{scen}-l_{l}')\n",
    "            alerts = pd.read_parquet(f'../../Data_and_models/data/alerts/{scen}.parquet')\n",
    "            test = alerts.loc[alerts['month'] == 7]\n",
    "            a = dict()\n",
    "            for direc in os.listdir(f'../../Data_and_models/testbed/{scen}-l_{l}/test/'):\n",
    "                if os.path.isfile(f'../../Data_and_models/testbed/{scen}-l_{l}/test/' + direc):\n",
    "                    continue\n",
    "                a[direc] = dict()\n",
    "                a[direc]['bat'] = pd.read_csv(f'../../Data_and_models/testbed/{scen}-l_{l}/test/' + direc + '/batches.csv')\n",
    "                a[direc]['cap'] = pd.read_csv(f'../../Data_and_models/testbed/{scen}-l_{l}/test/' + direc + '/capacity.csv')\n",
    "\n",
    "\n",
    "            alert_rate = float(scen.split('-')[0].split('_')[1])\n",
    "\n",
    "            seeds = os.listdir(f'../../Data_and_models/deferral/def_results_alert/{scen}-l_{l}/')\n",
    "            for seed in seeds:\n",
    "                if seed in ['Random','Full_Rej','Only_Classifier']:\n",
    "                    continue\n",
    "                models = ['DeCCaF','OvA']\n",
    "                if len(scen.split('-')) ==2:\n",
    "                    sub = 1\n",
    "                else:\n",
    "                    sub = float(scen.split('-')[2].split('_')[1])\n",
    "                    if l != 0.057:\n",
    "                        continue\n",
    "                    \n",
    "                for model in models:\n",
    "                        for test_env_id in a:\n",
    "                            #print(f'evaluating model {model} in {test_env_id}')\n",
    "                            direc = test_env_id\n",
    "                            test_env_id = test_env_id.split('#')\n",
    "                            \n",
    "                            batch_size = 4457\n",
    "                            batch_seed = 1\n",
    "\n",
    "                            if test_env_id[1].split('_')[0] == 'homogenous':\n",
    "                                distribution = 'homogenous'\n",
    "                                distribution_seed = 'NA'\n",
    "                                distribution_std = 'NA'\n",
    "                            else:\n",
    "                                distribution = 'variable'\n",
    "                                distribution_seed = test_env_id[1].split('_')[0].split('-')[1]\n",
    "                                distribution_std = '0.2'\n",
    "\n",
    "                            absence = 0\n",
    "                            absence_seed = 'NA'\n",
    "                            exp_pool = 'standard'\n",
    "                            deferral_rate = 0.9\n",
    "                                \n",
    "                        \n",
    "                            reviews = pd.read_parquet(f'../../Data_and_models/deferral/def_results_alert/{scen}-l_{l}/{seed}/{model}/{direc}/results.parquet')\n",
    "                            reviews = reviews['prediction']\n",
    "                            labels = test.loc[reviews.index,'fraud_bool']\n",
    "\n",
    "                            n_errors = (reviews != labels).astype(int).mean()\n",
    "                            tn, fp, fn, tp = confusion_matrix(y_true = labels, y_pred = reviews).ravel()\n",
    "                            tpr = tp/(tp+fn)\n",
    "                            fpr = fp/(fp+tn)\n",
    "                            \n",
    "                            sub_test = test.loc[reviews.index,:]\n",
    "                            old_ix = sub_test.loc[test['customer_age'] >= 50].index\n",
    "                            yng_ix = sub_test.loc[test['customer_age'] < 50].index\n",
    "\n",
    "\n",
    "                            old_pred = reviews.loc[old_ix]\n",
    "                            old_label = labels.loc[old_ix]\n",
    "                            fp_old = ((old_pred == 1) & (old_label == 0)).astype(int).sum()\n",
    "                            tn_old = ((old_pred == 0) & (old_label == 0)).astype(int).sum()\n",
    "\n",
    "                            yng_pred = reviews.loc[yng_ix]\n",
    "                            yng_label = labels.loc[yng_ix]\n",
    "                            fp_yng = ((yng_pred == 1) & (yng_label == 0)).astype(int).sum()\n",
    "                            tn_yng = ((yng_pred == 0) & (yng_label == 0)).astype(int).sum()\n",
    "\n",
    "                            fpr_yng = fp_yng/(fp_yng + tn_yng)\n",
    "                            fpr_old = fp_old/(fp_old + tn_old)\n",
    "\n",
    "                            fpr_disp =  fpr_yng/fpr_old\n",
    "                            test_env_df = test_env_df.append(pd.Series([scen,l,sub, alert_rate, seed,model,batch_size, \n",
    "                                                                        batch_seed, \n",
    "                                                                        absence,\n",
    "                                                                        absence_seed, \n",
    "                                                                        distribution, \n",
    "                                                                        distribution_std, \n",
    "                                                                        distribution_seed, \n",
    "                                                                        deferral_rate,\n",
    "                                                                        exp_pool,\n",
    "                                                                        n_errors,\n",
    "                                                                        tp,\n",
    "                                                                        fp,\n",
    "                                                                        fn,\n",
    "                                                                        tn,\n",
    "                                                                        tpr,\n",
    "                                                                        fpr, \n",
    "                                                                        fpr_disp], index = test_env_df.columns), ignore_index = True)\n",
    "\n",
    "            models = ['Random','Full_Rej','Only_Classifier']\n",
    "\n",
    "            for model in models:\n",
    "                        if len(scen.split('-')) ==2:\n",
    "                            sub = 1\n",
    "                        else:\n",
    "                            sub = float(scen.split('-')[2].split('_')[1])\n",
    "                            if l != 0.057:\n",
    "                                continue\n",
    "                        for test_env_id in a:\n",
    "                            direc = test_env_id\n",
    "                            test_env_id = test_env_id.split('#')\n",
    "                            batch_size = 4457\n",
    "                            batch_seed = 1\n",
    "\n",
    "                            if test_env_id[1].split('_')[0] == 'homogenous':\n",
    "                                distribution = 'homogenous'\n",
    "                                distribution_seed = 'NA'\n",
    "                                distribution_std = 'NA'\n",
    "                            else:\n",
    "                                distribution = 'variable'\n",
    "                                distribution_seed = test_env_id[1].split('_')[0].split('-')[1]\n",
    "                                distribution_std = '0.2'\n",
    "\n",
    "                            absence = 0\n",
    "                            absence_seed = 'NA'\n",
    "                            deferral_rate = 0.9\n",
    "                            exp_pool = 'standard'\n",
    "                            \n",
    "                            reviews = pd.read_parquet(f'../../Data_and_models/deferral/def_results_alert/{scen}-l_{l}/{model}/{direc}/results.parquet')\n",
    "                            reviews = reviews['prediction']\n",
    "                            labels = test.loc[reviews.index,'fraud_bool']\n",
    "\n",
    "                            n_errors = (reviews != labels).astype(int).mean()\n",
    "                            tn, fp, fn, tp = confusion_matrix(y_true = labels, y_pred = reviews).ravel()\n",
    "                            tpr = tp/(tp+fn)\n",
    "                            fpr = fp/(fp+tn)\n",
    "                            \n",
    "                            sub_test = test.loc[reviews.index,:]\n",
    "                            old_ix = sub_test.loc[test['customer_age'] >= 50].index\n",
    "                            yng_ix = sub_test.loc[test['customer_age'] < 50].index\n",
    "\n",
    "\n",
    "                            old_pred = reviews.loc[old_ix]\n",
    "                            old_label = labels.loc[old_ix]\n",
    "                            fp_old = ((old_pred == 1) & (old_label == 0)).astype(int).sum()\n",
    "                            tn_old = ((old_pred == 0) & (old_label == 0)).astype(int).sum()\n",
    "\n",
    "                            yng_pred = reviews.loc[yng_ix]\n",
    "                            yng_label = labels.loc[yng_ix]\n",
    "                            fp_yng = ((yng_pred == 1) & (yng_label == 0)).astype(int).sum()\n",
    "                            tn_yng = ((yng_pred == 0) & (yng_label == 0)).astype(int).sum()\n",
    "\n",
    "                            fpr_yng = fp_yng/(fp_yng + tn_yng)\n",
    "                            fpr_old = fp_old/(fp_old + tn_old)\n",
    "\n",
    "                            fpr_disp =  fpr_yng/fpr_old\n",
    "                            test_env_df = test_env_df.append(pd.Series([scen,l,sub,alert_rate, 'NA',model,batch_size, \n",
    "                                                                        batch_seed, \n",
    "                                                                        absence,\n",
    "                                                                        absence_seed, \n",
    "                                                                        distribution, \n",
    "                                                                        distribution_std, \n",
    "                                                                        distribution_seed, \n",
    "                                                                        deferral_rate,\n",
    "                                                                        exp_pool,\n",
    "                                                                        n_errors,\n",
    "                                                                        tp,\n",
    "                                                                        fp,\n",
    "                                                                        fn,\n",
    "                                                                        tn,\n",
    "                                                                        tpr,\n",
    "                                                                        fpr, \n",
    "                                                                        fpr_disp], index = test_env_df.columns), ignore_index = True)\n",
    "                            \n",
    "\n",
    "    test_env_df['cost/100 instances'] = test_env_df['fp']*test_env_df['cost'] + test_env_df['fn']\n",
    "    test_env_df['cost/100 instances'] = test_env_df['cost/100 instances'].astype('float')/(test_env_df['fp'] + test_env_df['fn'] +test_env_df['tp'] + test_env_df['tn']).astype('float')*100\n",
    "    test_env_df = test_env_df.replace('NA',np.nan)\n",
    "    test_env_df.to_parquet('../../Data_and_models/deferral/def_results_alert/results.parquet')\n",
    "else:\n",
    "    test_env_df = pd.read_parquet('../../Data_and_models/deferral/def_results_alert/results.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_env_df = test_env_df.loc[(test_env_df['sub'] == 1.0)|test_env_df['sub'].isna()]\n",
    "test_env_df['cost/100 instances'] = (test_env_df['fp']*test_env_df['cost'] + test_env_df['fn'])/(test_env_df['fp'] + test_env_df['tn'] + test_env_df['fn'] + test_env_df['tp'])*100\n",
    "a = test_env_df.groupby(['scen','cost','model','distribution', 'distribution_seed','training_seed'], dropna = False).mean().loc[:,['tpr','fpr','cost/100 instances']].round(2)\n",
    "a = a.reset_index()\n",
    "a_5 = a.loc[a['scen'] == 'alert_0.05-data_0.05']\n",
    "a_15 = a.loc[a['scen'] == 'alert_0.15-data_0.05']\n",
    "\n",
    "ova = test_env_df.loc[test_env_df['model'] == 'OvA']\n",
    "deccaf = test_env_df.loc[test_env_df['model'] == 'DeCCaF']\n",
    "ran = test_env_df.loc[test_env_df['model'] == 'Random']\n",
    "rej = test_env_df.loc[test_env_df['model'] == 'Full_Rej']\n",
    "aux = test_env_df.loc[test_env_df['model'] == 'Only_Classifier']\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8.695124778789568"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((deccaf.groupby(by = ['alert_rate','cost']).mean().reset_index().loc[:,'cost/100 instances'] - ova.groupby(by = ['alert_rate','cost']).mean().reset_index().loc[:,'cost/100 instances'])/ova.groupby(by = ['alert_rate','cost']).mean().reset_index().loc[:,'cost/100 instances']*100).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alert_rate</th>\n",
       "      <th>cost</th>\n",
       "      <th>Full_Rej</th>\n",
       "      <th>Only_Classifier</th>\n",
       "      <th>Random</th>\n",
       "      <th>OvA</th>\n",
       "      <th>DeCCaF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>$0.96$</td>\n",
       "      <td>$ 0.96$</td>\n",
       "      <td>$ 0.8 \\mbox{\\scriptsize{$\\pm 0.08$ }} $</td>\n",
       "      <td>$ 0.84 \\mbox{\\scriptsize{$\\pm 0.06$ }} $</td>\n",
       "      <td>$ 0.79 \\mbox{\\scriptsize{$\\pm 0.04$ }} $</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>$4.79$</td>\n",
       "      <td>$ 4.58$</td>\n",
       "      <td>$ 3.95 \\mbox{\\scriptsize{$\\pm 0.17$ }} $</td>\n",
       "      <td>$ 3.63 \\mbox{\\scriptsize{$\\pm 0.07$ }} $</td>\n",
       "      <td>$ 3.4 \\mbox{\\scriptsize{$\\pm 0.04$ }} $</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2850</td>\n",
       "      <td>$23.95$</td>\n",
       "      <td>$ 14.08$</td>\n",
       "      <td>$ 12.23 \\mbox{\\scriptsize{$\\pm 0.19$ }} $</td>\n",
       "      <td>$ 11.13 \\mbox{\\scriptsize{$\\pm 0.04$ }} $</td>\n",
       "      <td>$ 10.21 \\mbox{\\scriptsize{$\\pm 0.17$ }} $</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>$1.05$</td>\n",
       "      <td>$ 1.07$</td>\n",
       "      <td>$ 0.87 \\mbox{\\scriptsize{$\\pm 0.05$ }} $</td>\n",
       "      <td>$ 0.87 \\mbox{\\scriptsize{$\\pm 0.02$ }} $</td>\n",
       "      <td>$ 0.74 \\mbox{\\scriptsize{$\\pm 0.03$ }} $</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>$5.27$</td>\n",
       "      <td>$ 4.01$</td>\n",
       "      <td>$ 3.45 \\mbox{\\scriptsize{$\\pm 0.13$ }} $</td>\n",
       "      <td>$ 3.55 \\mbox{\\scriptsize{$\\pm 0.03$ }} $</td>\n",
       "      <td>$ 3.19 \\mbox{\\scriptsize{$\\pm 0.03$ }} $</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.2850</td>\n",
       "      <td>$26.36$</td>\n",
       "      <td>$ 6.93$</td>\n",
       "      <td>$ 6.0 \\mbox{\\scriptsize{$\\pm 0.14$ }} $</td>\n",
       "      <td>$ 5.23 \\mbox{\\scriptsize{$\\pm 0.08$ }} $</td>\n",
       "      <td>$ 4.85 \\mbox{\\scriptsize{$\\pm 0.09$ }} $</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alert_rate    cost Full_Rej Only_Classifier  \\\n",
       "0        0.05  0.0114   $0.96$         $ 0.96$   \n",
       "1        0.05  0.0570   $4.79$         $ 4.58$   \n",
       "2        0.05  0.2850  $23.95$        $ 14.08$   \n",
       "3        0.15  0.0114   $1.05$         $ 1.07$   \n",
       "4        0.15  0.0570   $5.27$         $ 4.01$   \n",
       "5        0.15  0.2850  $26.36$         $ 6.93$   \n",
       "\n",
       "                                      Random  \\\n",
       "0    $ 0.8 \\mbox{\\scriptsize{$\\pm 0.08$ }} $   \n",
       "1   $ 3.95 \\mbox{\\scriptsize{$\\pm 0.17$ }} $   \n",
       "2  $ 12.23 \\mbox{\\scriptsize{$\\pm 0.19$ }} $   \n",
       "3   $ 0.87 \\mbox{\\scriptsize{$\\pm 0.05$ }} $   \n",
       "4   $ 3.45 \\mbox{\\scriptsize{$\\pm 0.13$ }} $   \n",
       "5    $ 6.0 \\mbox{\\scriptsize{$\\pm 0.14$ }} $   \n",
       "\n",
       "                                         OvA  \\\n",
       "0   $ 0.84 \\mbox{\\scriptsize{$\\pm 0.06$ }} $   \n",
       "1   $ 3.63 \\mbox{\\scriptsize{$\\pm 0.07$ }} $   \n",
       "2  $ 11.13 \\mbox{\\scriptsize{$\\pm 0.04$ }} $   \n",
       "3   $ 0.87 \\mbox{\\scriptsize{$\\pm 0.02$ }} $   \n",
       "4   $ 3.55 \\mbox{\\scriptsize{$\\pm 0.03$ }} $   \n",
       "5   $ 5.23 \\mbox{\\scriptsize{$\\pm 0.08$ }} $   \n",
       "\n",
       "                                      DeCCaF  \n",
       "0   $ 0.79 \\mbox{\\scriptsize{$\\pm 0.04$ }} $  \n",
       "1    $ 3.4 \\mbox{\\scriptsize{$\\pm 0.04$ }} $  \n",
       "2  $ 10.21 \\mbox{\\scriptsize{$\\pm 0.17$ }} $  \n",
       "3   $ 0.74 \\mbox{\\scriptsize{$\\pm 0.03$ }} $  \n",
       "4   $ 3.19 \\mbox{\\scriptsize{$\\pm 0.03$ }} $  \n",
       "5   $ 4.85 \\mbox{\\scriptsize{$\\pm 0.09$ }} $  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.DataFrame()\n",
    "table = rej.groupby(by = ['alert_rate','cost']).mean().loc[:,'cost/100 instances'].reset_index().copy()\n",
    "\n",
    "table = table.rename(columns = {'cost/100 instances': 'Full_Rej'})\n",
    "table['Full_Rej'] = \"$\" + table['Full_Rej'].round(2).astype(str) + \"$\" \n",
    "table['Only_Classifier'] = \"$ \" + aux.groupby(by = ['alert_rate','cost']).mean().reset_index().loc[:,'cost/100 instances'].round(2).astype(str) + '$'\n",
    "table['Random'] = \"$ \"  + ran.groupby(by = ['alert_rate','cost']).mean().reset_index().loc[:,'cost/100 instances'].round(2).astype(str)  + ' \\mbox{\\scriptsize{$\\pm '+  (ran.groupby(by = ['alert_rate','cost']).std().reset_index().loc[:,'cost/100 instances']*1.96/np.sqrt(ran.groupby(by = ['alert_rate','cost']).count().reset_index().loc[:,'cost/100 instances'])).round(2).astype(str)+ \"$ }} $\"\n",
    "table['OvA'] =\"$ \"  + ova.groupby(by = ['alert_rate','cost']).mean().reset_index().loc[:,'cost/100 instances'].round(2).astype(str) + ' \\mbox{\\scriptsize{$\\pm '+  (ova.groupby(by = ['alert_rate','cost']).std().reset_index().loc[:,'cost/100 instances']*1.96/np.sqrt(ova.groupby(by = ['alert_rate','cost']).count().reset_index().loc[:,'cost/100 instances'])).round(2).astype(str)+ \"$ }} $\"\n",
    "table['DeCCaF'] = \"$ \"  +deccaf.groupby(by = ['alert_rate','cost']).mean().reset_index().loc[:,'cost/100 instances'].round(2).astype(str) + ' \\mbox{\\scriptsize{$\\pm '+  (deccaf.groupby(by = ['alert_rate','cost']).std().reset_index().loc[:,'cost/100 instances']*1.96/np.sqrt(deccaf.groupby(by = ['alert_rate','cost']).count().reset_index().loc[:,'cost/100 instances'])).round(2).astype(str)+ \"$ }} $\"\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alert_rate</th>\n",
       "      <th>cost</th>\n",
       "      <th>ova</th>\n",
       "      <th>Random</th>\n",
       "      <th>Full_Rej</th>\n",
       "      <th>Only_Classifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.2850</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0114</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0570</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.2850</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alert_rate    cost   ova  Random  Full_Rej  Only_Classifier\n",
       "0        0.05  0.0114  0.52    0.56      0.88             0.88\n",
       "1        0.05  0.0570  0.76    1.00      1.00             1.00\n",
       "2        0.05  0.2850  0.96    1.00      1.00             1.00\n",
       "3        0.15  0.0114  0.84    0.88      1.00             1.00\n",
       "4        0.15  0.0570  1.00    0.96      1.00             1.00\n",
       "5        0.15  0.2850  0.96    1.00      1.00             1.00"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp = pd.DataFrame(columns = ['alert_rate','cost','model','deccaf_is_better'])\n",
    "alert_rates = [0.05,0.15]\n",
    "\n",
    "for alert_rate in alert_rates:\n",
    "        for cost in cfg['costs']:\n",
    "            avg = 0\n",
    "            for seed in ['5k-1#regular','5k-2#regular','5k-3#regular','5k-4#regular','5k-5#regular']:\n",
    "                a = deccaf.loc[(deccaf['training_seed'] == seed) & (deccaf['cost'] == cost) & (deccaf['alert_rate'] == alert_rate)]\n",
    "                model_2 = ova.loc[(ova['training_seed'] == seed)& (ova['cost'] == cost) & (ova['alert_rate'] == alert_rate)]\n",
    "                avg += (a['cost/100 instances'].reset_index(drop = True) < model_2['cost/100 instances'].reset_index(drop = True)).astype(int).mean()\n",
    "            \n",
    "            avg = avg/5\n",
    "\n",
    "            comp = comp.append(pd.Series([alert_rate, cost, 'ova', avg], index = comp.columns), ignore_index = True)\n",
    "\n",
    "for model_n in ['Random','Full_Rej','Only_Classifier']:\n",
    "    model = test_env_df.loc[test_env_df['model'] == model_n]\n",
    "    \n",
    "    for alert_rate in alert_rates:\n",
    "        for cost in cfg['costs']:\n",
    "            avg = 0\n",
    "            for seed in ['5k-1#regular','5k-2#regular','5k-3#regular','5k-4#regular','5k-5#regular']:\n",
    "                a = deccaf.loc[(deccaf['training_seed'] == seed) & (deccaf['cost'] == cost) & (deccaf['alert_rate'] == alert_rate)]\n",
    "                model_2 = model.loc[(model['cost'] == cost) & (model['alert_rate'] == alert_rate)]\n",
    "                avg += (a['cost/100 instances'].reset_index(drop = True) < model_2['cost/100 instances'].reset_index(drop = True)).astype(int).mean()\n",
    "            \n",
    "            avg = avg/5\n",
    "\n",
    "            comp = comp.append(pd.Series([alert_rate, cost, model_n, avg], index = comp.columns), ignore_index = True)\n",
    "\n",
    "temp = comp.iloc[:6,:2]\n",
    "temp['ova'] = comp.loc[comp['model'] == 'ova']['deccaf_is_better']\n",
    "temp['Random'] = comp.loc[comp['model'] == 'Random']['deccaf_is_better'].reset_index(drop = True)\n",
    "temp['Full_Rej'] = comp.loc[comp['model'] == 'Full_Rej']['deccaf_is_better'].reset_index(drop = True)\n",
    "temp['Only_Classifier'] = comp.loc[comp['model'] == 'Only_Classifier']['deccaf_is_better'].reset_index(drop = True)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_env_df = pd.read_parquet('../../Data_and_models/deferral/def_results_alert/results.parquet')\n",
    "test_env_df = test_env_df.loc[test_env_df['cost'] == 0.057]\n",
    "test_env_df['cost/100 instances'] = (test_env_df['fp']*test_env_df['cost'] + test_env_df['fn'])/(test_env_df['fp'] + test_env_df['tn'] + test_env_df['fn'] + test_env_df['tp'])*100\n",
    "a = test_env_df.loc[(test_env_df['deferral_rate'] == 0.9)].groupby(['scen','alert_rate','cost','model','distribution', 'distribution_seed','training_seed','sub'], dropna = False).mean().loc[:,['tpr','fpr','cost/100 instances']].round(2)\n",
    "a = a.reset_index()\n",
    "a = a.drop(a.loc[a['sub'].isna()].index)\n",
    "a = a.drop(a.loc[a['model'] == 'OvA-default'].index)\n",
    "a_5 = a.loc[a['alert_rate'] == 0.05]\n",
    "a_5 = a_5.drop(a_5.loc[(a_5['model'] == 'Full_Rej')].index)\n",
    "a_5 = a_5.drop(a_5.loc[(a_5['model'] == 'Random')].index)\n",
    "a_5 = a_5.drop(a_5.loc[(a_5['model'] == 'Only_Classifier')].index)\n",
    "a_5['Deferral'] = a_5['model']\n",
    "a_15 = a.loc[a['alert_rate'] == 0.15]\n",
    "a_15 = a_15.drop(a_15.loc[(a_15['model'] == 'Full_Rej')].index)\n",
    "a_15 = a_15.drop(a_15.loc[(a_15['model'] == 'Random')].index)\n",
    "a_15 = a_15.drop(a_15.loc[(a_15['model'] == 'Only_Classifier')].index)\n",
    "a_15['Deferral'] = a_15['model']\n",
    "\n",
    "f, ax = plt.subplots(figsize=(6, 3), ncols=2, sharey = True, layout = 'tight')\n",
    "sns.lineplot(ax = ax[0], data = a_5, x = 'sub', y = 'cost/100 instances', hue = 'Deferral', markers = True, hue_order=['OvA','DeCCaF'], marker = 'o', errorbar = (\"ci\",95),err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "sns.lineplot(ax = ax[1], data = a_15, x = 'sub', y = 'cost/100 instances', hue = 'Deferral', markers = True, hue_order=['OvA','DeCCaF'], marker = 'o', err_style = 'bars', err_kws={\"capsize\": 2})\n",
    "ax[0].set_title(\"Alert Rate = 0.05\")\n",
    "ax[1].set_title(\"Alert Rate = 0.15\")\n",
    "plt.ylim([3.1,4.3])\n",
    "for x in ax:\n",
    "    x.set_xlabel(r'% of Total Training Data')\n",
    "    x.set_xticks(np.arange(0.25,1.02,0.25))\n",
    "    x.set_ylabel(r'$\\mathcal{C}$ / 100 instances')\n",
    "    x.set_xlim([0.23,1.02])\n",
    "    \n",
    "plt.savefig(\"Cost-sub.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.calibration import calibration_curve\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "scens = ['alert_0.05-data_0.05','alert_0.15-data_0.05']\n",
    "costs = [0.0114,0.057,0.285]\n",
    "\n",
    "auc_ece_model = pd.DataFrame()\n",
    "\n",
    "for scen in scens:\n",
    "    for l in costs:\n",
    "        alert_rate = float(scen.split('-')[0].split('_')[1])\n",
    "        if len(scen.split('-')) == 2:\n",
    "            sub = 1\n",
    "        else:\n",
    "            sub = float(scen.split('-')[2].split('_')[1])\n",
    "\n",
    "        a = pd.read_parquet(f'../../Data_and_models/testbed/{scen}-l_{l}/test/test_expert_pred.parquet')['model#0']\n",
    "        b = pd.read_parquet(f'../../Data_and_models/testbed/{scen}-l_{l}/test/test.parquet')['fraud_label']\n",
    "\n",
    "        data = pd.read_parquet(f'../../Data_and_models/data/alerts/{scen}.parquet')\n",
    "        val = data.loc[data['month'] == 6]\n",
    "        e_c = val['fraud_bool'].replace([0,1], [l,1]).mean()\n",
    "        reb_1 = (val['fraud_bool'].mean()/e_c)\n",
    "        reb_0 = (1-val['fraud_bool']).mean()*l/e_c\n",
    "        test = pd.read_parquet(f'../../Data_and_models/testbed/{scen}-l_{l}/test/test.parquet')\n",
    "        nmin = len(test.loc[test['fraud_label'] == 0])\n",
    "        nmax = int(nmin*reb_1/reb_0)\n",
    "        oversampled = pd.concat([test.loc[test['fraud_label'] == 0], test.loc[test['fraud_label'] == 1].sample(replace=True, n = nmax, random_state=42)]).index\n",
    "        outcomes = b.loc[oversampled]\n",
    "        prob_true, prob_pred = calibration_curve(y_true = outcomes, y_prob = a.loc[oversampled], strategy='quantile', n_bins = 10)\n",
    "\n",
    "        auc_ece_model.loc[f'{scen}-l_{l}','alert_rate'] = alert_rate\n",
    "        auc_ece_model.loc[f'{scen}-l_{l}','cost'] = l\n",
    "        auc_ece_model.loc[f'{scen}-l_{l}','roc'] = roc_auc_score(y_true = outcomes, y_score = a.loc[oversampled])\n",
    "        auc_ece_model.loc[f'{scen}-l_{l}','ece'] = np.mean(np.abs(prob_true - prob_pred))*100\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alert_rate</th>\n",
       "      <th>cost</th>\n",
       "      <th>roc</th>\n",
       "      <th>ece</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alert_0.05-data_0.05-l_0.0114</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.698</td>\n",
       "      <td>1.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alert_0.05-data_0.05-l_0.057</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.707</td>\n",
       "      <td>4.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alert_0.05-data_0.05-l_0.285</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.706</td>\n",
       "      <td>4.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alert_0.15-data_0.05-l_0.0114</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.757</td>\n",
       "      <td>3.777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alert_0.15-data_0.05-l_0.057</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.754</td>\n",
       "      <td>4.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alert_0.15-data_0.05-l_0.285</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.732</td>\n",
       "      <td>3.281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               alert_rate   cost    roc    ece\n",
       "alert_0.05-data_0.05-l_0.0114        0.05  0.011  0.698  1.110\n",
       "alert_0.05-data_0.05-l_0.057         0.05  0.057  0.707  4.844\n",
       "alert_0.05-data_0.05-l_0.285         0.05  0.285  0.706  4.550\n",
       "alert_0.15-data_0.05-l_0.0114        0.15  0.011  0.757  3.777\n",
       "alert_0.15-data_0.05-l_0.057         0.15  0.057  0.754  4.196\n",
       "alert_0.15-data_0.05-l_0.285         0.15  0.285  0.732  3.281"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc_ece_model.round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = pd.read_parquet('../../Data_and_models/data/BAF_deployment_score.parquet')\n",
    "from sklearn.metrics import roc_curve\n",
    "f, ax = plt.subplots(figsize = (4.5,4.5))\n",
    "fpr, tpr, thresholds = roc_curve(a['fraud_bool'], a['model_score'])\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.plot([0,1],[0,1], color = 'gray', linestyle = '--')\n",
    "plt.xlim([-0.01,1.01])\n",
    "plt.ylim([-0.01,1.01])\n",
    "plt.title(\"ROC-AUC Curve - Alert Model - Months 4-8\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.plot(fpr,tpr)\n",
    "plt.savefig(\"ROC-AUC_Alert_model.pdf\", format=\"pdf\", bbox_inches=\"tight\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}